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Single-query Path Planning Using Sample-efficient Probability Informed Trees

IEEE Robotics and Automation Letters, Volume 6, Number 3, page 4624--4631 — jun 2021
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In this work, we present a novel sampling-based path planning method, called SPRINT. The method finds solutions for high dimensional path planning problems quickly and robustly. Its efficiency comes from minimizing the number of collision check samples. This reduction in sampling relies on heuristics that predict the likelihood that samples will be useful in the search process. Specifically, heuristics (1) prioritize more promising search regions; (2) cull samples from local minima regions; and (3) steer the search away from previously observed collision states. Empirical evaluations show that our method finds shorter or comparable-length solution paths in significantly less time than commonly used methods. We demonstrate that these performance gains can be largely attributed to our approach to achieve sample efficiency.

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BibTex references

  author       = "Rakita, Daniel and Mutlu, Bilge and Gleicher, Michael",
  title        = "Single-query Path Planning Using Sample-efficient Probability Informed Trees",
  journal      = "IEEE Robotics and Automation Letters",
  number       = "3",
  volume       = "6",
  pages        = "4624--4631",
  month        = "jun",
  year         = "2021",
  doi          = "https://doi.org/10.1109/LRA.2021.3068682",
  url          = "http://graphics.cs.wisc.edu/Papers/2021/RMG21"

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